TY - GEN
T1 - Opportunities and Challenges in Developing COVID-19 Simulation Models
T2 - 2021 Annual Modeling and Simulation Conference, ANNSIM 2021
AU - Giabbanelli, Philippe J.
AU - Badham, Jennifer
AU - Castellani, Brian
AU - Kavak, Hamdi
AU - Mago, Vijay
AU - Negahban, Ashkan
AU - Swarup, Samarth
N1 - Funding Information:
PJG is supported by a philanthropic grant from Microsoft AI for Health. In the UK, JB and BC thank the Medical Research Council (MC_PC_18045) and the Economic and Social Research Council; team members were also supported by the Economic and Social Research Council (ES/S000402/1) while publication fees were made possible via an IBM grant for supporting open access COVID-19 research. In the US, HK is supported by the National Science Foundation (DEB-2030685) and internal funds at George Mason University for summer research (ASSIP 2020-21, STIP 2021). In Canada, VM was funded by the Lakehead University (Romeo #1467916) and the Northern Ontario Academic Medical Association Fund (C-14-2-18). In the USA, AN is supported by the Institute for Computational and Data Sciences at The Pennsylvania State University and computational resources provided by a Google Cloud COVID-19 Research Grant. SS thanks the National Science Foundation (CCF-1918656) and a Defense Threat Reduction Agency subcontract/ARA (S-D00189-15-TO-01-UVA). The authors are collectively indebted to many students, colleagues, and collaborators who contributed to the Covid-19 projects and enabled the reflections in this paper.
Publisher Copyright:
© 2021 SCS.
PY - 2021/7/19
Y1 - 2021/7/19
N2 - The COVID-19 pandemic showed us the importance of modeling and forecasting efforts to guide decision makers. However, a year into the COVID-19 pandemic, the computational science literature lacks a proper internal exploration of the modeling journey of researchers around the world, including how they responded to the shared challenges our community faced such as data limitations, model fitting and working with public stakeholders. The current paper is a detailed examination of the internal processes of six research teams, which were funded in several countries to model COVID-19. Each team was asked to reflect on the research question and how they solved their respective modeling challenges, as well as how, looking back, they would do things differently.
AB - The COVID-19 pandemic showed us the importance of modeling and forecasting efforts to guide decision makers. However, a year into the COVID-19 pandemic, the computational science literature lacks a proper internal exploration of the modeling journey of researchers around the world, including how they responded to the shared challenges our community faced such as data limitations, model fitting and working with public stakeholders. The current paper is a detailed examination of the internal processes of six research teams, which were funded in several countries to model COVID-19. Each team was asked to reflect on the research question and how they solved their respective modeling challenges, as well as how, looking back, they would do things differently.
UR - http://www.scopus.com/inward/record.url?scp=85117382919&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85117382919&partnerID=8YFLogxK
U2 - 10.23919/ANNSIM52504.2021.9552089
DO - 10.23919/ANNSIM52504.2021.9552089
M3 - Conference contribution
AN - SCOPUS:85117382919
T3 - Proceedings of the 2021 Annual Modeling and Simulation Conference, ANNSIM 2021
BT - Proceedings of the 2021 Annual Modeling and Simulation Conference, ANNSIM 2021
A2 - Martin, Cristina Ruiz
A2 - Blas, Maria Julia
A2 - Psijas, Alonso Inostrosa
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 19 July 2021 through 22 July 2021
ER -